Comparing smooth transition and Markov switching autoregressive models of US unemployment

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Comparing smooth transition and Markov switching autoregressive models of US unemployment

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Philippe J. Deschamps (Séminaire d'Économétrie, Université de Fribourg, Switzerland)

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Philippe J. Deschamps

Abstract

Logistic smooth transition and Markov switching autoregressive models of a logistic transform of the monthly US unemployment rate are estimated by Markov chain Monte Carlo methods. The Markov switching model is identified by constraining the first autoregression coefficient to differ across regimes. The transition variable in the LSTAR model is the lagged seasonal difference of the unemployment rate. Out-of-sample forecasts are obtained from Bayesian predictive densities. Although both models provide very similar descriptions, Bayes factors and predictive efficiency tests (both Bayesian and classical) favor the smooth transition model. Copyright © 2008 John Wiley & Sons, Ltd.

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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 23 (2008)
Issue (Month): 4 ()
Pages: 435-462
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Handle: RePEc:jae:japmet:v:23:y:2008:i:4:p:435-462

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Paper
- Philippe J. Deschamps, 2007. "Comparing smooth transition and Markov switching autoregressive models of US Unemployment," DQE Working Papers 7, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland, revised 04 Jun 2008. [Downloadable!]

References listed on IDEAS
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Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

David Peel & Ivan Paya & E Pavlidis, 2009. "Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form," Working Papers 005913, Lancaster University Management School, Economics Department. [Downloadable!]

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